The project is organized into the following key components:
## Configuration
# Configuration
1.***configs.py***
- Defines an `EvalConfig` class for storing configurations for evaluating multiple recommendation models.
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@@ -122,13 +122,10 @@ The system supports the following regression models for predicting user ratings:
-`lightgbm`
# Datasets
# Data folder
1.***small***
- Contains traditional MovieLens data and is used for evaluating models and building our recommendation system.
- Contains traditional MovieLens data and is used for evaluating models and building our recommendation system based on ratings and movie data.
2.***test***
- A smaller dataset (6 users and 10 items) used for understanding algorithm workings during model development.and how algorithms work during model development.
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@@ -138,15 +135,28 @@ The system supports the following regression models for predicting user ratings:
### Web Page - Overview
# Web Page - Overview
Our web page serves as the user interface for the Recommender System. It allows users to interact with the recommendation models & view recommended items. It was built in backend powered by Flask (Python) to handle requests and serve recommendations.
1.***Features***
- Home
- Discovery
- Search
1.***Home***
The Home page provides a set of recommendations for the user based on user-based, content-based, and latent factor model algorithms.
###### pages folder
2.***Discovery***
The Discover page allows the user to find recommendations based on characteristics inherent to the movies themselves.
3.***Search***
The Search page allows users to search the general database and apply filters.
